Semantic Scene Segmentation for Robotics
Deep Learning for Robot Perception and Cognition(2024)
摘要
Comprehensive scene understanding is a critical enabler of robot autonomy.
Semantic segmentation is one of the key scene understanding tasks which is
pivotal for several robotics applications including autonomous driving,
domestic service robotics, last mile delivery, amongst many others. Semantic
segmentation is a dense prediction task that aims to provide a scene
representation in which each pixel of an image is assigned a semantic class
label. Therefore, semantic segmentation considers the full scene context,
incorporating the object category, location, and shape of all the scene
elements, including the background. Numerous algorithms have been proposed for
semantic segmentation over the years. However, the recent advances in deep
learning combined with the boost in the computational capacity and the
availability of large-scale labeled datasets have led to significant advances
in semantic segmentation. In this chapter, we introduce the task of semantic
segmentation and present the deep learning techniques that have been proposed
to address this task over the years. We first define the task of semantic
segmentation and contrast it with other closely related scene understanding
problems. We detail different algorithms and architectures for semantic
segmentation and the commonly employed loss functions. Furthermore, we present
an overview of datasets, benchmarks, and metrics that are used in semantic
segmentation. We conclude the chapter with a discussion of challenges and
opportunities for further research in this area.
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关键词
semantic scene segmentation,robotics
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